Programming Pearls

Bentley's classic, "Programming Pearls", makes an important point, namely that you won't get good performance without careful coding and profile-based tuning. And it's made clearly, concisely and with compelling examples. The choice of language (C), and the choice of problems (those from computer science 101 we all think we know cold) betrays the sophistication of Bentley's analyses. Suppose, for the sake of argument, that you have a binary search that's holding up your loop. Or your Huffman coding just isn't snappy enough? "How is that possible?", you might say, fresh out of computer-science 201, "Didn't we just prove these algorithms are optimal?" Well yes, asymptotically up to an arbitrary constant multiplier. But this is the real world, and your code needs to go faster. If this sounds like your predicament, pull up a chair and read "Programming Pearls"; if it's not, you might wonder what all the fuss is about.Next, fire up your favorite hardware (Sparc or x86 or PowerPC), favorite language (Perl, Java, or even C), favorite release of that language, along with your favorite interpreter or compiler (Hotspot or standard? GCC or Visual C++). And you'll need a profiler; might as well treat yourself to a good one if you're serious. Then fire up your code with a representative range realistic test data and observe what happens. Function by function, byte by byte. Then try to be as clever as Bentley in (a) figuring out why, (b) trying a range of alternatives, and (c) making it all go faster with minor tuning. Typically, you'll find a single bottleneck taking an order of magnitude more time than everything else, and work on that. Repeat until fast enough.As well as this simple, yet surprisingly effective and realistic methodology, Bentley provides a range of concrete tips on making things go faster, from tweaking data structures to unfolding loops (especially precomputing low-order cases) to using accumulators and caching, all with an eye to underlying memory, communication and CPU resources.Real code that has to run fast, like the code that we write at my current company for signal processing, speech recognition and speech synthesis, typically looks like the end-product of Bentley's refactorings. And it gets that way following exactly the path he lays out: analyze the problem, choose the right algorithm (or the right few to evaluate), and then tune it up using profiling."Programming Pearls" is the beginning of the road. You will need to look elsewhere for topics such as compression for memory saving, numerical algorithms, effective concurrency and memory sharing, efficient buffered I/O, garbage collection, and the wide range of dynamic programming and heuristic techniques.

This book is timeless because it discusses recurring problem situations with elegance, clarity, and insight. The book is about thinking and problem-solving more than it is about the particular circumstances it discusses.For instance, the very first chapter ("Cracking the Oyster") would seem to be about the problem of sorting on disk: surely an archaic concern in these days of 1+GB RAM and 100 GB online media on PCs. But that would entirely miss the point, which Bentley clearly summarizes for us in the "principles" section of this chapter: * "Defining the problem was 90 percent of this battle..." * Select an appropriate data structure * Consider multiple-pass algorithms * A simple design: "a designer knows he has arrived at perfection not when there is no longer anything to add, but when there is no longer anything to take away." -- St. ExuperyThis advice might look like a string of old, worn-out chestnuts as set forth above. But within the context of the specific problem, we can see how the design challenges and solutions follow each other, through several iterations, culminating in a pretty solution, nicely illustrating the principles, and suggesting their relevance to other problems, too.A thoughtful programmer, no matter whether her domain is machine language or OODBMSes, will come away from any chapter in this book full of new ideas and inspiration.Problems (good ones) after each section encourage the kind of rumination that is necessary to derive the most from this book. Every few years I take it (and its companion, "More Programming Perals") off the shelf and dip into it again, and always come away enlightened.

The thirteen columns in this book appeared in the Communications of the ACM between 1983 and 1985. There can't be more than a couple of technical books on computing from that era that are still worth reading. Kernighan & Ritchie's book, "The C Programming Language", is one that springs to mind; this book is definitely another, and will probably outlast K & R as it has almost no ties to existing or past hardware or languages.What Bentley does in each of these columns is take some part of the field of programming--something that every one of us will have run into at some point in our work--and dig underneath it to reveal the part of the problem that is permanent; that doesn't change from language to language. The first two parts cover problem definition, algorithms, data structures, program verification, and efficiency (performance, code tuning, space tuning); the third part applies the lessons to example pseudocode, looking at sorting, searching, heaps, and an example spellchecker.Bentley writes clearly and enthusiastically, and the columns are a pleasure to read. But the reason so many people love this book is not for the style, it's for the substance--you can't read this book and not come away a better programmer. Inefficiency, clumsiness, inelegance and obscurity will offend you just a little more after you've read it.It's hard to pick a favourite piece, but here's one nice example from the algorithm design column that shows how little the speed of your Pentium matters if you don't know what you're doing. Bentley presents a particular problem (the details don't matter) and multiple different ways to solve it, calculating the relationship between problem size and run time for each algorithm. He gives, among others, a cubic algorithm (run time equal to a constant, C, times the cube of the problem size, N--i.e. t ~ CN^3), and a linear algorithm with constant K (t ~ KN). He then implemented them both: the former in fine-tuned FORTRAN on a Cray-1 supercomputer; the latter in BASIC on a Radio Shack TRS-80. The constant factors were as different as they could be, but with increasing problem size the TRS-80 eventually has to catch up--and it does. He gives a table showing the results: for a problem size of 1000, the Cray takes three seconds to the TRS-80's 20 seconds; but for a problem size of 1,000,000, the TRS-80 takes five and a half hours, whereas the Cray would take 95 years.The book is informative, entertaining, and will painlessly make you a better programmer. What more can you ask?

Without any doubt, my favorite article in _Communications of the ACM_ in the 1980's was the regular `Programming Pearls' articles by Jon Bentley. When the first edition of these collected gems was published, I read it with great delight. Now, over a decade later, a second edition has been published, containing the same problems with additional modifications and notations. Given the enormous changes in programming since the mid 80's, your first reaction might be that this book is dated and therefore irrelevant. Nothing could be further from the truth. Elegant solutions to complex programming problems are free from the rot of time. Programming is a thought process largely independent of the notation used to write it down. The solutions are sketched and explained rather than coded, and the solutions are complete. There is a certain mystique about taking a complex problem, finding an initial solution and then refining it down until it kicks some big time. There are some major lessons in program refinement explained in these solutions. Coding a binary search is covered quite extensively, which may seem like a waste of space, as this problem was solved decades ago. However, that solution took decades to get right, and this is one of those "separates the coders from the key bangers" type of problem. Other problems examined include performance tuning, squeezing space and program correctness. While the improvement in the performance of the hardware has been astounding since these solutions were written, that does not make them obsolete. The complexity of the programs that we now build has risen even faster, so performance and space considerations are just as critical. Some problems were here at the beginning and will still be here at the end. Even though there may be canned code to handle them, these problems are generic enough that the solutions can be applied elsewhere, so we must learn how to solve them. Understanding these problems and their solutions will give you a fundamental skill set that will serve you well for a long time.

It's great to see they've come out with an update to this book. The essays in this book are easy to read and touch on many valuable things, such as tuning and optimization of algorithms, using mini languages to provide robust tools, doing back-of-the-envelope calculations, and much more. I have recommended this book to several beginning programmers that I know as an excellent introduction to thinking effectively about the challenges of software engineering.